1 code implementation • ECCV 2020 • Tongyao Pang, Yuhui Quan, Hui Ji
In recent years, deep learning emerges as one promising technique for solving many ill-posed inverse problems in image recovery, and most deep-learning-based solutions are based on supervised learning.
no code implementations • CVPR 2023 • Xinran Qin, Yuhui Quan, Tongyao Pang, Hui Ji
To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time.
no code implementations • CVPR 2023 • Yuhui Quan, Zicong Wu, Hui Ji
Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one.
1 code implementation • ICCV 2023 • Yutao Jiang, Yang Zhou, Yuan Liang, Wenxi Liu, Jianbo Jiao, Yuhui Quan, Shengfeng He
To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference.
no code implementations • ICCV 2023 • Yuhui Quan, Haoran Huang, Shengfeng He, Ruotao Xu
Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing.
no code implementations • ICCV 2023 • Yuhui Quan, Huan Teng, Ruotao Xu, Jun Huang, Hui Ji
This paper proposes a fingerprinting framework for DNN models of image restoration.
1 code implementation • ICCV 2023 • Yuhui Quan, Xin Yao, Hui Ji
Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs).
1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2022 • Jinxiu Liang, Yong Xu, Yuhui Quan, Boxin Shi, Hui Ji
The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment.
no code implementations • 1 May 2022 • Qiaoqiao Ding, Hui Ji, Yuhui Quan, Xiaoqun Zhang
Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation.
1 code implementation • NeurIPS 2021 • Yong Xu, Feng Li, Zhile Chen, Jinxiu Liang, Yuhui Quan
Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation.
1 code implementation • NeurIPS 2021 • Yuhui Quan, Zicong Wu, Hui Ji
Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount.
Ranked #4 on Image Defocus Deblurring on RealDOF
no code implementations • CVPR 2021 • Zhile Chen, Feng Li, Yuhui Quan, Yong Xu, Hui Ji
In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition.
1 code implementation • CVPR 2021 • Tongyao Pang, Huan Zheng, Yuhui Quan, Hui Ji
Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising.
no code implementations • 1 Jan 2021 • Tongyao Pang, Yuhui Quan, Hui Ji
Built on the Bayesian neural network (BNN), this paper proposed a self-supervised deep learning method for denoising a single image, in the absence of training samples.
1 code implementation • 21 Jul 2020 • Jinxiu Liang, Jingwen Wang, Yuhui Quan, Tianyi Chen, Jiaying Liu, Haibin Ling, Yong Xu
REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
no code implementations • 4 Jul 2020 • Jinxiu Liang, Yong Xu, Yuhui Quan, Jingwen Wang, Haibin Ling, Hui Ji
Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.
1 code implementation • CVPR 2020 • Yuhui Quan, Mingqin Chen, Tongyao Pang, Hui Ji
In last few years, supervised deep learning has emerged as one powerful tool for image denoising, which trains a denoising network over an external dataset of noisy/clean image pairs.
no code implementations • ICCV 2019 • Yuhui Quan, Shijie Deng, Yixin Chen, Hui Ji
It is a challenging problem to remove the effect of raindrops from an image.
no code implementations • 10 Nov 2018 • Ruotao Xu, Yuhui Quan, Yong Xu
Aiming at separating the cartoon and texture layers from an image, cartoon-texture decomposition approaches resort to image priors to model cartoon and texture respectively.
no code implementations • ICCV 2017 • Guodong Xu, Yuhui Quan, Hui Ji
This paper addresses the problem of defocus map estimation from a single image.
no code implementations • CVPR 2016 • Yuhui Quan, Yong Xu, Yuping Sun, Yan Huang, Hui Ji
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.
no code implementations • CVPR 2016 • Yuhui Quan, Chenglong Bao, Hui Ji
Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e. g. dynamic texture (DT).
no code implementations • ICCV 2015 • Yuhui Quan, Yan Huang, Hui Ji
In addition, based on the proposed dictionary learning method, a DT descriptor is developed, which has better adaptivity, discriminability and scalability than the existing approaches.
no code implementations • CVPR 2014 • Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen
Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem.
no code implementations • CVPR 2014 • Yuhui Quan, Yong Xu, Yuping Sun, Yu Luo
Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes.